Papers › Discovering Weight Initializers with Meta Learning
Discovering Weight Initializers with Meta Learning
Dmitry Baranchuk, Artem Babenko
Deep neural network training largely depends on the choice of initial weight distribution. However, this choice can often be nontrivial. Existing theoretical results for this problem mostly cover simple architectures, e.g., feedforward networks with ReLU activations. The architectures used for practical problems are more complex and often incorporate many overlapping modules, making them challenging for theoretical analysis. Therefore, practitioners have to use heuristic initializers with questionable optimality and stability. In this study, we propose a task-agnostic approach that discovers initializers for specific network architectures and optimizers by learning the initial weight distributions directly through the use of Meta-Learning. In several supervised and unsupervised learning scenarios, we show the advantage of our initializers in terms of both faster convergence and higher model performance.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections